AI-Assisted Endometriosis Diagnosis: A Multi-CNN Laparoscopic Image Analysis

Endometriosis is a chronic and painful disorder that significantly affects many aspects of a woman’s life. Its complex symptomatology makes early diagnosis and effective treatment particularly challenging. Although deep Convolutional Neural Networks (CNNs) have shown promise in medical image classification, few studies have explored their application to endometriosis detection. This study evaluates and compares the performance of three advanced CNN models (DesNet121, InceptionV3, and Xception) using laparoscopic images to identify endometriotic tissue. A custom dataset was compiled by merging images from the ENDI and GLENDA databases, with preprocessing steps including normalization and data augmentation. The models were trained and validated using stratified splits, and assessed based on standard metrics (accuracy, precision, recall, AUC, and confusion matrices). The results revealed accuracy scores of 89%, 91%, and 97% for ResNet121, InceptionV3, and Xception, respectively, with Xception demonstrating the highest performance. This approach offers a potential tool for clinicians, aiming to accelerate diagnosis, reduce the rate of misdiagnosis, and improve patient outcomes. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only Similar content being viewed by others

Koninckx, P.R., Ussia, A., Adamyan, L., Wattiez, A., Donnez, J.: Deep endometriosis: definition, diagnosis, and treatment. Fertil. Steril. 98(3), 564–571 (2012) Melin, A., Sparen, P., Persson, I., Bergqvist, A.: Endometriosis and the risk of cancer with special emphasis on ovarian cancer. Hum. Reprod. 21(5), 1237–1242 (2006) Kennedy, S., et al.: ESHRE guideline for the diagnosis and treatment of endometriosis. Hum. Reprod. 20(10), 2698–2704 (2005) Leibetseder, A., Schoeffmann, K., Keckstein, J., Keckstein, S.: Endometriosis detection and localization in laparoscopic gynecology. Multimedia Tools Appl. 81(5), 6191–6215 (2022) Zhang, Y., et al: Deep learning model for classifying endometrial lesions. J. Transl. Med. 19, 1–13 (2021) Guerriero, S., et al.: Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis. Eur. J. Obstet. Gynecol. Reprod. Biol. 261, 29–33 (2021) Visalaxi, S., Muthu, T.S.: Automated prediction of endometriosis using deep learning. Int. J. Nonlinear Anal. Appl. 12(2), 2403–2416 (2021) Leibetseder, A., Kletz, S., Schoeffmann, K., Keckstein, S., Keckstein, J.: GLENDA: gynecologic laparoscopy endometriosis dataset. In: Ro, Y.M., et al. (eds.) MMM 2020, Part II. LNCS, vol. 11962, pp. 439–450. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_36 Leibetseder, A., et al.: Endometriosis detection and localization in laparoscopic gynecology. Multimedia Tools Appl. 81(5), 6191–6215 (2022). https://doi.org/10.1007/s11042-021-11730-1 Takahashi, Y., et al.: Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy. PLoS One 16(3), e0248526 (2021) Macis, C., et al.: A convolutional neural network tool for early diagnosis and precision surgery in endometriosis-associated ovarian cancer. Appl. Sci. 15(6), 3070 (2025) Zaidi, S.A., Chouvatut, V., Phongnarisorn, C., Praserttitipong, D.: Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation. Intell.-Based Med. 11, 100230 (2025) Pascoal, E., et al.: Strengths and limitations of diagnostic tools for endometriosis and relevance in diagnostic test accuracy research. Ultrasound Obstet. Gynecol. 60(3), 309–327 (2022) Allaire, C., Bedaiwy, M.A., Yong, P.J.: Diagnosis and management of endometriosis. CMAJ 195(10), E363–E371 (2023) Gratton, S.M., et al.: Diagnosis of endometriosis at laparoscopy: a validation study comparing surgeon visualization with histologic findings. J. Obstet. Gynaecol. Canada 44(2), 135–141 (2022) Chauhan, S., More, A., Chauhan, V., Kathane, A., Chauhan Sr, V.V.: Endometriosis: a review of clinical diagnosis, treatment, and pathogenesis. Cureus 14(9) (2022) Kaveh, M., et al.: The impact of early diagnosis of endometriosis on quality of life. Arch. Gynecol. Obstet. 1–7 (2025) Amro, B., et al.: New understanding of diagnosis, treatment and prevention of endometriosis. Int. J. Environ. Res. Public Health 19(11), 6725 (2022) Vitale, S.G., et al.: Risk of endometrial cancer in asymptomatic postmenopausal women in relation to ultrasonographic endometrial thickness: systematic review and diagnostic test accuracy meta-analysis. Am. J. Obstet. Gynecol. 228(1), 22–35 (2023) Smolarz, B., Szyłło, K., Romanowicz, H.: Endometriosis: epidemiology, classification, pathogenesis, treatment and genetics (review of literature). Int. J. Mol. Sci. 22(19), 10554 (2021) Author information Authors and Affiliations Corresponding author Editor information Editors and Affiliations Rights and permissions Copyright information © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG About this paper Cite this paper Murillo-Guanuchy, D. et al. (2026). AI-Assisted Endometriosis Diagnosis: A Multi-CNN Laparoscopic Image Analysis. In: Martínez, L., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2025. IDEAL 2025. Lecture Notes in Computer Science, vol 16239. Springer, Cham. https://doi.org/10.1007/978-3-032-10489-2_14 Download citation DOI: https://doi.org/10.1007/978-3-032-10489-2_14 Published: Publisher Name: Springer, Cham Print ISBN: 978-3-032-10488-5 Online ISBN: 978-3-032-10489-2 eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science